Agentic Workflow for Automatic Population of Study Data Tabulation Model (SDTM) Datasets

Manual SDTM mapping is a sequential, programmer-intensive process taking 4-8 weeks per study. A custom agentic workflow automates the interpretation of CRF annotations, application of CDISC standards, and generation of define.xml. By orchestrating rule-based mapping agents and LLMs for complex variable derivation, the pipeline cuts cycle time to 1-2 weeks, accelerating downstream analysis and submission readiness.

SDTM programming consumes significant FTE hours, creating a variable cost center sensitive to study volume and programmer turnover. Automating the repetitive mapping logic allows a smaller, more senior team to focus on exception handling, complex derivations, and strategic oversight. This shifts the operating model from manual coding to automated pipeline management, protecting margins and improving scalability for portfolio growth.

Manual mapping introduces inconsistencies and validation errors that are costly to fix late in the process. A custom workflow embeds continuous validation checks against CDISC rules and sponsor standards. Agents flag discrepancies in variable labels, formats, and controlled terminology during dataset creation, not after. This proactive quality layer reduces rework, minimizes technical rejections, and creates cleaner data for regulatory review.

Regulators require clear traceability from source data to submission tables. A manual process relies on documented programmer decisions. An automated workflow logs every mapping rule application, LLM reasoning step, and validation outcome as an immutable audit trail. This creates a defensible, machine-readable lineage from raw EDC extracts to final SDTM datasets, streamlining preparation for regulatory inspections and data integrity audits.

The delay between database lock and SDTM delivery is a critical path for CSR assembly and ISS/ISE. Automating dataset generation decouples this timeline from programmer availability. Once raw data is locked, the orchestrated agent pipeline can execute mapping, validation, and output generation in a predictable, scheduled run. This compresses the lock-to-analysis window, bringing key efficacy and safety insights forward for faster decision-making.

A custom SDTM automation workflow is not a one-study solution. It codifies sponsor-specific standards, therapeutic area conventions, and CDISC implementations into a reusable, version-controlled asset. This ensures consistency across all studies in a program, reduces onboarding time for new programmers, and creates a scalable operating model for global submissions. The architecture becomes a strategic platform, not just a tactical script.

This primary agent ingests annotated Case Report Forms (CRFs) and data specifications (e.g., in PDF, DOCX, or Define-XML). Using a combination of rule-based parsing and LLM reasoning, it interprets variable labels, codelists, and derivations to generate initial SDTM domain mappings (e.g., mapping VISITDT to --DTC in the Findings domain). It flags ambiguous mappings for human review and outputs a structured mapping specification for the transformation engine.

A rule-based agent continuously validates the emerging datasets and metadata against the current CDISC SDTM Implementation Guide (IG) and therapeutic area user guides. It checks for conformance in variable names, formats, controlled terminology, and domain-specific rules. Violations are logged in a validation report with direct references to the IG, enabling programmers to correct issues preemptively rather than during final QC.

This is the core execution layer that applies the validated mapping logic to raw clinical data (typically SAS datasets or CSV files from EDC systems like Medidata Rave or Veeva EDC). It performs data type conversions, unit harmonization, and derives SDTM-specific variables (e.g., --DY from --DTC). It operates within a containerized environment (e.g., Python/Pandas, Spark) for scalability and integrates with clinical data warehouses.

Upon dataset generation, this agent automatically creates the accompanying define.xml metadata file. It extracts variable attributes, value-level metadata, and codelist information from the transformation process to produce a machine-readable define.xml that is fully synchronized with the datasets. This eliminates the manual, error-prone process of maintaining separate metadata documents.

This orchestrator agent manages the workflow's human-in-the-loop processes. It routes flagged issues from the mapping and validation agents to a centralized queue (e.g., in Jira or a custom dashboard) for statistical programmer review. It also triggers automated re-runs of transformations after corrections and manages version control for datasets and define.xml, ensuring a complete audit trail.

This component handles the secure integration with source systems (EDC, Labs) and target repositories (e.g., submission archive, clinical data repository). It manages authentication, data transfer protocols (SFTP, API), and final deployment of the validated SDTM package. It also generates operational reports on pipeline performance, dataset lineage, and processing time for governance and continuous improvement.